Abstract

Habitat evaluation is essential for managing wildlife populations and formulating conservation policies. With the rise of innovative powerful statistical techniques in partnership with Remote Sensing, GIS and GPS techniques, spatially explicit species distribution modeling (SDM) has rapidly grown in conservation biology. These models can help us to study habitat suitability at the scale of the species range, and are particularly useful for examining the overlapping habitat between sympatric species. Species presence points collected through field GPS observations, in conjunction with 13 different topographic, vegetation related, anthropogenic, and bioclimatic variables, as well as a land cover map with seven classification categories created by support vector machine (SVM) were used to implement Maxent and GARP ecological niche models. With the resulting ecological niche models, the suitable habitat for asiatic black bear (Ursus thibetanus) and red panda (Ailurus fulgens) in Nepal Makalu Barun National Park (MBNP) was predicted. All of the predictor variables were extracted from freely available remote sensing and publicly shared government data resources. The modeled results were validated by using an independent dataset. Analysis of the regularized training gain showed that the three most important environmental variables for habitat suitability were distance to settlement, elevation, and mean annual temperature. The habitat suitability modeling accuracy, characterized by the mean area under curve, was moderate for both species when GARP was used (0.791 for black bear and 0.786 for red panda), but was moderate for black bear (0.857), and high for red panda (0.920) when Maxent was used. The suitable habitat estimated by Maxent for black bear and red panda was 716 km2 and 343 km2 respectively, while the suitable area determined by GARP was 1074 km2 and 714 km2 respectively. Maxent predicted that the overlapping area was 83% of the red panda habitat and 40% of the black bear habitat, while GARP estimated 88% of the red panda habitat and 58% of the black bear habitat overlapped. The results of land cover exhibited that barren land covered the highest percentage of area in MBNP (36.0%) followed by forest (32.6%). Of the suitable habitat, both models indicated forest as the most preferred land cover for both species (63.7% for black bear and 61.6% for red panda from Maxent; 59.9% black bear and 58.8% for red panda from GARP). Maxent outperformed GARP in terms of habitat suitability modeling. The black bear showed higher habitat selectivity than red panda. We suggest that proper management should be given to the overlapping habitats in the buffer zone. For remote and inaccessible regions, the proposed methods are promising tools for wildlife management and conservation, deserving further popularization.

Highlights

  • In recent years, several statistical and computer-based methods have been utilized to map biological and ecological data and to spatially interpolate species distributions and other bio-spatial variables of interest

  • The Asiatic black bear (Ursus thibetanus) and Himalayan red panda (Ailurus fulgens) are two threatened species that are recorded in Makalu Barun National Park (MBNP) along with other areas of Nepal

  • The main objective of this study was to: (1) predict the suitable habitat for Asiatic black bear and red panda in Makalu Barun National Park based on presence data and a range of environmental variables by using Maxent and Genetic Algorithm for Rule set Production (GARP); (2) compare the results of two models for the individual species and determine the extent of the overlapping suitable habitat of these two species in the study area; and (3) propose recommendations and analysis for the conservation of the two species in MBNP

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Summary

Introduction

Several statistical and computer-based methods have been utilized to map biological and ecological data and to spatially interpolate species distributions and other bio-spatial variables of interest. Maxent is a software for modeling species niches and distribution that applies a machine learning technique called maximum entropy modeling, which is for modeling geographic distributions of a species based on the ecological niche theory proposed by J­aynes[17] This method was initially employed to estimate the density of presence across the l­andscape[9], relying on information from species presence data to explore the possible distribution of a target species within a study area. Previous studies related to the distribution, diet, habitat, and threats of the Asiatic black bear and red panda have examined the two species separately These species, live in habitats that have similar altitudinal ­ranges[27,28,29,30] and are sympatric in many protected areas of Nepal including ­MBNP31. Such evaluation constitutes a footstone in wildlife protection and management, offering a scientific rationale for the improvement of conservation p­ olicies[44]

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